Utilizing a neural network model to generate a reference image based on a combination of images

ABSTRACT

A device may receive complex data from a user device and may provide multiple images to the user device based on receiving the complex data. The device may receive, from the user device, a selection of two or more images from the multiple images, and may determine whether a combination of the two or more images is stored in a data structure. The device may determine a mapping of information identifying the two or more images with the complex data, based on the combination of the two or more images not being stored in the data structure, and may store the information identifying the two or more images, the complex data, and the mapping in the data structure. The device may process the two or more images to generate a reference image that satisfies a memorability score threshold and may provide the reference image to another user device.

BACKGROUND

Memorability may indicate a likelihood that an image will be rememberedby a user (e.g., by being stored in a short-term memory or a long-termmemory of the user). A memorability score of the image may correspond toa percentage of users that remember the image after the image has beenpresented multiple times. The memorability score may be used todetermine a measure of effectiveness of the image with respect to theusers.

SUMMARY

In some implementations, a method may include receiving complex datafrom a user device and providing a plurality of images to the userdevice based on receiving the complex data. The method may includereceiving, from the user device, a selection of two or more images fromthe plurality of images and determining whether a combination of the twoor more images is stored in a data structure. The method may includedetermining a mapping of the two or more images with the complex data,based on the combination of the two or more images not being stored inthe data structure, and storing information identifying the two or moreimages, the complex data, and the mapping in the data structure. Themethod may include processing the two or more images to generate areference image that satisfies a memorability score threshold andproviding the reference image to another user device. The method mayinclude receiving, from the other user device, a selection of the two ormore images, and retrieving the complex data from the data structurebased on the mapping and based on the selection of the two or moreimages. The method may include performing one or more actions based onthe complex data.

In some implementations, a device includes one or more memories and oneor more processors to receive complex data from a user device, whereinthe complex data includes one or more of: a uniform resource locator, atelephone number, or textual information, and provide a plurality ofimages to the user device based on receiving the complex data. The oneor more processors may receive, from the user device, a selection of twoor more images from the plurality of images and may determine whether acombination of the two or more images is stored in a data structure. Theone or more processors may determine a mapping of the two or more imageswith the complex data, based on the combination of the two or moreimages not being stored in the data structure, and may store informationidentifying the two or more images, the complex data, and the mapping inthe data structure. The one or more processors may process the two ormore images to generate a reference image that satisfies a memorabilityscore threshold and may provide the reference image to another userdevice. The one or more processors may receive, from the other userdevice, a selection of the two or more images, and may retrieve thecomplex data from the data structure based on the mapping and based onthe selection of the two or more images. The one or more processors mayperform one or more actions based on the complex data.

In some implementations, a non-transitory computer-readable medium maystore a set of instructions that includes one or more instructions that,when executed by one or more processors of a device, cause the device toreceive complex data from a user device, and provide a plurality ofimages to the user device based on receiving the complex data. The oneor more instructions may cause the device to receive, from the userdevice, a selection of two or more images from the plurality of imagesand determine whether a combination of the two or more images is storedin a data structure. The one or more instructions may cause the deviceto determine a mapping of the two or more images with the complex data,based on the combination of the two or more images not being stored inthe data structure, and store information identifying the two or moreimages, the complex data, and the mapping in the data structure. The oneor more instructions may cause the device to process the two or moreimages to generate a reference image that satisfies a memorability scorethreshold and provide the reference image to another user device.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A-1F are diagrams of an example implementation described herein.

FIG. 2 is a diagram illustrating an example of training and using amachine learning model in connection with generating a reference imagebased on complex data.

FIG. 3 is a diagram of an example environment in which systems and/ormethods described herein may be implemented.

FIG. 4 is a diagram of example components of one or more devices of FIG.3.

FIG. 5 is a flowchart of an example process for utilizing a neuralnetwork model to generate a reference image based on complex data.

DETAILED DESCRIPTION

The following detailed description of example implementations refers tothe accompanying drawings. The same reference numbers in differentdrawings may identify the same or similar elements.

A length of a uniform resource locator (URL) may be shortened to createa shortened URL. For example, a URL (e.g.,“http://en.wikipedia.org/wiki/Test Data”) may be shortened to ashortened URL (e.g., “https://bit.ly/1sNZMwL”). Current techniques forshortening URLs utilize computing resources, networking resources, amongother resources. Although a length of a shortened URL is less than alength of a URL, the shortened URL may be more difficult for users toremember than the URL. Furthermore, the shortened URL may includecharacters that are visually similar (e.g., “0,” “O,” and “o”; “1,” “l,”and “I”; and “8” and “B”; and so on). Such visually similar charactersare confusing to users and difficult for users to distinguish.Additionally, the shortened URL may be subject to an increased riskassociated with phishing attacks.

Therefore, current techniques for URL shortening waste computingresources (e.g., processing resources, memory resources, communicationresources, among other examples), networking resources, and/or otherresources associated with generating a shortened URL that is notmemorable and confusing for users, causing a device to access anincorrect network resource associated with a shortened URL (when thecharacters of the shortened URL are erroneously input into the device),causing a device to access a malicious network resource associated witha phishing attack, taking remedial actions against the phishing attacks,among other examples.

Some implementations described herein relate to a reference system thatutilizes a neural network model to generate a reference image based on acombination of images. For example, the reference system may receivecomplex data from a user device and may provide a plurality of images tothe user device based on receiving the complex data. The complex datamay include a uniform resource locator, a telephone number, textualinformation, among other examples. The reference system may receive,from the user device, a selection of two or more images from theplurality of images and may determine whether a combination of the twoor more images is stored in a data structure. For example, the referencesystem may determine whether the data structure stores a mapping of thetwo or more images with other complex data.

Based on the combination of the two or more images not being stored inthe data structure, the reference system may determine a mapping of thetwo or more images with the complex data and may store the two or moreimages, the complex data, and the mapping in the data structure. Thereference system may process the two or more images, with a neuralnetwork model, to generate a reference image that satisfies amemorability score threshold and may provide the reference image toanother user device. In some implementations, the reference image mayonly be displayed (e.g., to a user of the other device) to help the userremember the information needed to retrieve the complex data (e.g., tohelp the user remember the two or more images). In some examples, thereference image may cover (or include) information and/or featuresincluded in the two or more images. The reference system may receive,from the other user device, a selection of the two or more images, andmay retrieve the complex data from the data structure based on themapping and based on the selection of the two or more images.

The reference system may perform one or more actions based on thecomplex data. For example, in a situation where the complex datacorresponds to a URL, the reference system may cause a web page,associated with the URL, to be provided to the other user device basedon selection of the reference image. Additionally, or alternatively, ina situation where the complex data corresponds to a telephone number,the reference system may cause a call to a telephone number, associatedwith the complex data, to be established with the other user device.Additionally, or alternatively, in a situation where the complex datacorresponds to text, the reference system may provide text, associatedwith the complex data, to the other user device.

As described herein, the reference system utilizes a neural networkmodel to generate a reference image based on a combination of images.For example, the reference image may be a memorable image that may helpthe user select the complex data (e.g., help the user to remember thetwo or more images that may be used to retrieve the complex data). Bygenerating the reference image in this manner, the reference systemconserves computing resources (e.g., processing resources, memoryresources, communication resources, among other examples), networkingresources, and/or other resources that would otherwise have beenconsumed by generating a shortened URL that is not memorable and isconfusing for users, causing a device to access an incorrect networkresource associated with a shortened URL (when the characters of the URLare erroneously input into the device), causing a device to access amalicious network resource associated with a phishing attack, takingremedial actions against the phishing attacks, among other examples.

FIGS. 1A-1F are diagrams of an example 100 associated with utilizing aneural network model to generate a reference image based on acombination of images. As shown in FIGS. 1A-1F, example 100 includes auser device, a reference system, and a data structure (e.g., a database,a table, a linked list, among other examples). The user device mayinclude a laptop computer, a mobile telephone, a desktop computer, amongother examples. The user device may be associated with a user.

The reference system may include one or more devices that utilize aneural network model to generate a reference image associated with acombination of images. The reference image may include a combination ofdifferent images (e.g., a combination of content of different images).The complex data may include a uniform resource locator, a telephonenumber, textual information, among other examples. In some examples, thereference image may be used to enable users to access the complex data.

The data structure may store a plurality of categories of images thatmay be used by the reference system to generate reference imagesassociated with different complex data. The plurality of categories ofimages may include a first category of images (e.g., images of colors),a second category of images (e.g., images of animals), a third categoryof images (e.g., image of fruits), and a fourth category of images(e.g., images of locations), and so on. In some examples, each image maybe associated with information identifying content of the image (e.g.,metadata identifying the content). In some implementations, thereference system may use the information identifying the content toidentify complex data, as described in more detail below.

Additionally, or alternatively, the data structure may store informationidentifying two or more images (of the plurality of categories ofimages) in association with different complex data (e.g., a firstmapping of two or more first images and first complex data, a secondmapping of two or more second images and second complex data, and soon), among other examples. In some implementations, the plurality ofcategories of images may include one or more images or one or morecategories of images provided to the data structure by a device of anadministrator of the data structure, by the reference system, by theuser device, by another device of the user, among other examples. Insome implementations, the information identifying the two or more imagesin association with the different complex data may be provided to thedata structure by the reference system.

As shown in FIG. 1A, and by reference number 105, the reference systemmay receive, from the user device, complex data. For example, assumethat the user desires to create a reference image associated withcomplex data and that the complex data includes a uniform resourcelocator (URL). As shown in FIG. 1A, the URL may be“https://Reference-nal.website.com/people-have-good-response-to-things”).Further assume that the user desires to create the reference image toprevent the user and/or one or more other users (associated with theuser) from becoming victims of a phishing attack associated with theURL. Additionally, or alternatively, assume that the user desires tocreate the reference image to prevent the user and/or the one or moreother users from incorrectly inputting the URL (thereby preventingaccess to an incorrect network resource). The user and the one or moreusers may be members of a same organization. The user device mayprovide, to the reference system, a request to generate the referenceimage associated with the URL. The reference system may receive (e.g.,from the user device) the request to generate the reference imageassociated with the URL. In some examples, the request may includeinformation identifying the URL, information identifying the user, amongother examples.

As shown in FIG. 1A, and by reference number 110, the reference systemmay provide a plurality of images to the user device based on receivingthe complex data. For example, based on receiving the request (e.g.,including the URL), the reference system may obtain the plurality ofimages from the data structure and provide the plurality of images tothe user device. In some examples, the reference system may cause theplurality of images to be displayed by the user device (e.g., via a userinterface). As shown in FIG. 1A, for example, the plurality of imagesmay include the first category of images (images of colors), the secondcategory of images (images of animals), and the third category of images(images of fruits).

In some implementations, the plurality of images may be a subset ofimages stored by the data structure. By obtaining and providing thesubset of images, the reference system may conserve computing resources,network resources, and/or storage resources that would have otherwisebeen consumed to obtain and provide an entirety of the images stored bythe data structure.

In some examples, the reference system may identify the plurality ofimages based on the information identifying the user (e.g., included inthe request) and use the information identifying the user to perform alookup of the data structure. In some implementations, the datastructure may store information identifying one or more categories ofimages in association with information identifying different users(e.g., information identifying one or more first categories of images inassociation with information identifying a first group of users,information identifying one or more second categories of images inassociation with information identifying a second group of users, and soon). For example, the reference system may determine (e.g., based onhistorical data) that the one or more first categories of images arecategories of images that are most memorable to the first group ofusers. For example, the one or more first categories of images mayinclude images of vehicles and the first group of users may be maleusers. The reference system may determine (e.g., based on historicaldata) that the one or more second categories of images are categories ofimages that are most memorable to the second group of users. Forexample, the one or more second categories of images may include imagesof video games and the second group of users may be video gamers.

In some examples, the one or more first categories of images may bedifferent than the one or more second categories of images. For example,the one or more first categories of images may include images of colors,images of animals, and images of fruits and the one or more secondcategories of images may include images of colors, images of vehicles,images of clothes, and images of shoes.

The reference system may use the information identifying the user toperform a lookup of the data structure and obtain the one or morecategories of images associated with the information identifying theuser. The plurality of images, provided to the user device, may includethe one or more categories of images associated with the informationidentifying the user.

As shown in FIG. 1B, and by reference number 115, the reference systemmay receive, from the user device, a selection of two or more imagesfrom the plurality of images. For example, after providing the pluralityof images to the user device, the reference system may provide, to theuser device, a selection request to select images from the plurality ofimages. The user device may select the two or more images from theplurality of images and the reference system may receive, from the userdevice, information identifying the two or more images selected by theuser. In some implementations, the user device may select a quantity ofimages and cause the reference system to select the two or more imagesfrom the plurality of images based on the quantity of images.

In some implementations, the quantity of images (selected by the userdevice) may be based on a measure of frequency of usage (or a measure ofpopularity) of the URL. In some examples, the reference system mayanalyze the URL and/or perform a search of web documents to determinethe measure of frequency of usage (or the measure of popularity) of theURL. For example, the reference system may provide a request to selecttwo (2) images if the URL is a frequently used URL, three (3) images ifthe URL is a less frequently used URL, four (4) images if the URL iseven less frequently used, and so on. Alternatively, the referencesystem may provide a request to select four (4) images if the URL is afrequently used URL, three (3) images if the URL is a less frequentlyused URL, two (2) images if the URL is even less frequently used, and soon. In some examples, the reference system may provide a request toselect two (2) images to represent a domain name associated with theURL.

As shown in FIG. 1B, assume the user device selects four (4) images: animage of the color blue, an image of a panda, an image of a lemon, andan image of the color pink. Thus, the user device may provide, and thereference system may receive, information identifying the four (4)images.

As shown in FIG. 1B, and by reference number 120, the reference systemmay determine whether a combination of the two or more images is storedin the data structure. For example, based on receiving the informationidentifying the one or more images (from the user device), the referencesystem may perform a lookup of the data structure to determine whetherthe one or more images (e.g., the combination of the one or more images)are associated (in the data structure) with other complex data that isdifferent than the complex data.

In some implementations, when the reference system determines that thecombination of the two or more images) are associated with other complexdata in the data structure, the reference system may provide, to theuser device, a selection request to select two or more new images fromthe plurality of images. Alternatively, the reference system mayprovide, to the user device, a selection request to select two or morenew images from the plurality of images when the reference systemdetermines that another combination of images is within a particularlevel of similarity with respect to the combination of two or moreimages (e.g., a percentage of the images (in the combination of the twoor more images) is included in the images of the other combination ofimages). For example, the reference system may determine that thecombination of purple, panda, lemon, and pink is similar to thecombination of blue, panda, lemon, and pink. some percentage of theimages has to be different (e.g., two of the four need to be different).In some examples, the reference system may provide informationidentifying the other combination of images when providing the pluralityof images (after determining that the one or more images are associatedwith other complex data). In some examples, after the user deviceselects two or more images, the reference system may disable images thatmay create a combination of images that is similar to other combinationof images stored in the data structure.

Alternatively to providing the selection request to select two or morenew images from the plurality of images, the reference system mayprovide, to the user device, a selection request to select two or morenew images from a different plurality of images. Additionally, oralternatively, the reference system may provide, to the user device, aselection request to select an image from the plurality of images and animage from a different plurality of images.

As shown in FIG. 1C, and by reference number 125, the reference systemmay determine a mapping of information identifying the two or moreimages with the complex data, when the combination of the two or moreimages is not stored in the data structure, and may store theinformation identifying the two or more images, the complex data, andthe mapping in the data structure. For example, assume that thecombination of the two or more images (or the selected two or moreimages) are not associated (in the data structure) with other complexdata. The reference system may generate the mapping of the informationidentifying the two or more images with the complex data (e.g., theURL). In some examples, the mapping may correlate the two or more imageswith the URL. The reference system may provide the mapping to the datastructure for storage. In some implementations, the mapping may includeinformation identifying the content of the two or more images (e.g.,metadata of the two or more images). As an example, the mapping mayadditionally or alternatively associate the words blue, panda, lemon,and pink with the URL.

As shown in FIG. 1D, and by reference number 130, the reference systemmay process the two or more images, with a neural network model, togenerate a reference image that satisfies a memorability scorethreshold. In some implementations, the reference system may combine theone or more images in different manners to generate a plurality ofcombined images. For example, when generating the plurality of combinedimages, the reference system may combine the color blue and the panda togenerate an image of a blue panda, combine the color pink and the pandato generate an image of a pink panda, combine the color blue and a lemonto generate an image of a blue lemon, combine the color pink and a lemonto generate an image of a pink lemon, combine the image of the bluepanda and the image of the blue lemon, combine the image of the pinkpanda and the image of the blue lemon, among other examples.Additionally, or alternatively, the plurality of combined images mayinclude an image with an arrangement of blue panda (on a left side) andpink lemon (on a right side), an image with an arrangement of pink panda(on a left side) and blue lemon (on a right side), an image with pinkpanda of a first size, an image with pink panda of a second size, amongother examples.

The reference system may use the neural network model to process theplurality of combined images. In some implementations, the neuralnetwork model may be trained to predict measures of memorability (e.g.,memorability scores) of different images. The neural network model mayinclude a residual neural network (ResNet) model, a deep learningtechnique (e.g., a faster regional convolutional neural network (R-CNN))model, a feedforward neural network model, a radial basis functionneural network model, a Kohonen self-organizing neural network model, arecurrent neural network (RNN) model, a convolutional neural networkmodel, a modular neural network model, a deep learning image classifierneural network model, a Convolutional Neural Networks (CNNs) model,among other examples.

In some implementations, the neural network model may be trained usingtraining data (e.g., historical and/or current) as described below inconnection with FIG. 2. In some examples, the training data may includedifferent images, data regarding features of the different images,content category data regarding categories (e.g., of content) identifiedby the different images, data regarding different exposure times for thedifferent images to users, time interval between exposures of thedifferent images, information indicating whether the users rememberedthe different images, among other examples.

The features of an image (of the different images) may include acontrast of the image, a color of the image, a saturation of the image(e.g., a color saturation of the image), a size of the image (e.g., aheight and/or a width of the image and/or an aspect ratio of the image),a position of one or more portions of the image, a sharpness of theimage, a brightness of the image, a blurriness of the image, among otherexamples. The categories (identified by the different images) mayinclude goods, services, among other examples. The exposure time mayrefer to a period of time during which the different images is exposed(or presented) to the users.

The reference system may train the neural network model in a mannersimilar to the manner described below in connection with FIG. 2.Alternatively, rather than training the neural network model, thereference system may obtain the neural network model from another systemor device that trained the neural network model. In this case, the othersystem or device may obtain the training data (discussed above) for usein training the neural network model, and may periodically receiveadditional data that the other system or device may use to retrain orupdate the neural network model.

In some examples, the reference system may provide the plurality ofcombined images as an input to the neural network model and the neuralnetwork model may determine (or predict), as an output, memorabilityscores for the plurality of combined images. For example, the referencesystem may provide a first combined image as an input to the neuralnetwork model and may use the neural network model to determine amemorability score for the first combined image, provide a secondcombined image as an input to the neural network model and may use theneural network model to determine a memorability score for the secondcombined image, and so on. A memorability score, of a combined image,may indicate a likelihood of the combined image being remembered afterthe combined image has been viewed.

In some implementations, the reference system may compare thememorability scores and the memorability threshold and may identify amemorability score that satisfies the memorability score threshold. Thememorability score threshold may be based on data (e.g., historicaland/or current) regarding memorability score thresholds, based oninformation included in the request from the user device, among otherexamples. In some examples, if the reference system determines thatmultiple memorability scores satisfy the memorability score threshold,the reference system may identify a memorability score that is a highestscore out of the multiple memorability scores that satisfy thememorability score threshold. In some implementations, the referencesystem may select one of the plurality of combined images (e.g.,associated with the selected memorability score) as the reference image.

As shown in FIG. 1D, the reference image may include a pink panda (on aleft side of the reference image) and a blue lemon (on a right side ofthe reference image). In some implementations, the reference system mayprovide the reference image to the user device to confirm that thereference image is to be selected as the reference image for the URL.The reference system may receive, from the user device, informationindicating that the reference image is to be selected as the referenceimage for the URL and, accordingly, may cause the reference image to bestored in the data structure. As example, information regarding thereference image may be stored in association with the mapping of theinformation identifying the two or more images and the URL.

In some implementations, the reference system may receive, from the userdevice, a modification to the reference image (e.g., a selection of oneor more other images to replace the one or more images previouslyselected by the user device). In such an instance, the reference systemmay perform the actions described above (in connection with referencenumbers 120, 125, and 130) to generate a modified image (e.g., a newreference image).

As shown in FIG. 1E, and by reference number 135, the reference systemmay provide the reference image to another user device and via awebsite. For example, assume that the reference image was not modifiedand that the user of the user device desires to provide the referenceimage to the other user device. For example, assume that the userdesires to provide the reference image to prevent a phishing attackassociated with the URL and/or to prevent the other user device fromaccessing an incorrect network resource (e.g., by incorrectly inputtingthe URL). The user device may cause the reference system to provide thereference image to the user device. In some implementations, thereference image may be provided via the website. As an example, thewebsite may be an internal website of the organization.

As shown in FIG. 1E, and by reference number 140, the reference systemmay receive, from the other user device, a selection of the two or moreimages. For example, assume that a particular user of the other userdevice (e.g., the one or more other users or the user) desires to accessthe network resource associated with the URL. The particular user maycause the other user device to select the two or more images. Forexample, the other user device may click on the two or more images, theother user device may detect a tactile input (from the particular user)on a display of the two or more images (e.g., the particular user maytap the two or more images provided on the display), among otherexamples. In some implementations, the reference image may be providedto the other user device to enable the particular user to remember thecombination of the two or more images that may be used to retrieve thecomplex data. In some examples, selection of the two or more images maycause the other user device to provide a request, for the URL, to thereference system. In some examples, the request may include informationidentifying the combination of the two or more images (e.g., the wordspink, panda, blue, lemon), the information identifying the two or moreimages, among other examples.

As shown in FIG. 1E, and by reference number 145, the reference systemmay retrieve the complex data from the data structure based on themapping and the selection of the two or more images. For example, thereference system may receive the request, from the other user device,based on the selection of the two or more images. Based on receiving therequest, the reference system may use the information regarding theselection of the two or more images to perform a lookup of the datastructure. Based on the performing the lookup of the data structure andbased on the mapping, the reference system may obtain the complex data(e.g., the URL) from the data structure. The reference system mayperform one or more actions based on obtaining the complex data, asdescribed below.

In some implementations, the particular user may desire to input (e.g.,via the other user device) a shortened URL corresponding to the URLinstead of selecting the final image. For example, assume that thereference image is not accessible and that the particular user remembersthe reference image. In such an instance, the particular user may input(e.g., via the website or via a user interface), as part of theshortened URL, information that the particular user remembers about thereference image. For example, the particular user may input a stringthat includes the words pink, panda, blue, and lemon, or a portion ofeach of the words (e.g., in any order desired by the user). As anexample, the particular user may input https://xx.xx/Pink Panda iseating blue lemon, https://xx.xx/LemonBluePandaPink, among otherexamples.

The other user device may cause the shortened URL to be provided to thereference system. The reference system may receive the shortened URL andanalyze the shortened URL to identify the words pink, panda, blue, andlemon. The reference system may perform a lookup of the data structureusing a combination of the words pink, panda, blue, and lemon. Based onthe performing the lookup, the reference system may determine thecombination of the words pink, panda, blue, and lemon matchesinformation included in the mapping (e.g., matches the informationregarding the one or more images) which is stored in association withthe complex data). Accordingly, the reference system may obtain thecomplex data (e.g., the URL) based on performing the lookup. In someimplementations, the reference system may be configured to correctmisspellings of the words included in the shortened URL prior toperforming the lookup. In some implementations, the reference system maybe configured to perform a lookup of the data structure to identify thecomplex data when the shortened URL provides an incomplete descriptionof the reference image. For example, the reference system may performthe lookup to identify one or more mappings that include the incompletedescription and provide information regarding the one or more mappingsto the other user device to enable the particular user to provide acomplete description of the reference image. For example, the shortenedURL does not include text corresponding to one of the two or moreimages, the reference system may provide (e.g., to the other userdevice) a sequence of images (including the image) to enable the otheruser device to select the image. By selecting the image, the referencesystem may include a text (associated with the image) in the shortenedURL and use the shortened URL to access the complex data, as describedabove.

As shown in FIG. 1F, and by reference number 135, the reference systemmay perform one or more actions based on the complex data. In someimplementations, the one or more actions include the reference systemaccessing a website via the URL associated with the complex data andproviding content of the website to the other user device. For example,the reference system may determine that the complex data is a URL. Basedon determining that the complex data is a URL, the reference system mayaccess the website using the URL and cause the content of the website tobe provided to the other user device.

In some implementations, the one or more actions include the referencesystem causing a call to a telephone number associated with the complexdata to be established with the other user device. For example, thereference system may determine that the complex data is a telephonenumber. Based on determining that the complex data is a URL, thereference system may cause a call to be established with the other userdevice associated with the telephone number.

In some implementations, the one or more actions include the referencesystem providing text associated with the complex data to the other userdevice. For example, the reference system may determine that the complexdata is text. Based on determining that the complex data is text, thereference system may cause the text to be provided to the other userdevice. For example, the text may be provided via a user interface.

In some implementations, the one or more actions include the referencesystem receiving feedback associated with the selection of the two ormore images based on the complex data. For example, the reference systemmay receive the feedback from the other user device. In some instances,the feedback may indicate that the reference image is not memorable andmay identify one or more changes to the reference images to improve amemorability of the reference image. The one or more changes mayindicate a different type of content is more related to the URL (e.g.,locations and vehicles instead of animals and fruits), may indicate thatone or more features (e.g., a contrast, a size, among other examples) ofthe reference image are to be modified, among other examples. In someimplementations, the reference system may modify the reference imagebased on the feedback as described above.

In some implementations, the one or more actions include the referencesystem retraining the neural network model based on the complex data.The reference system may utilize the complex data as additional trainingdata for retraining the neural network model, thereby increasing thequantity of training data available for training the neural networkmodel. Accordingly, the reference system may conserve computingresources associated with identifying, obtaining, and/or generatinghistorical data for training the neural network model relative to othersystems for identifying, obtaining, and/or generating historical datafor training machine learning models. Additionally, or alternatively,utilizing the complex data as additional training data improves theaccuracy and efficiency of the neural network model, thereby conservingcomputing resources (e.g., processing resources, memory resources,communication resources, and/or the like), networking resources, and/orother resources that would have otherwise been used if the neuralnetwork model was not updated.

By generating the reference image as described herein, the referencesystem conserves computing resources (e.g., processing resources, memoryresources, communication resources, among other examples), networkingresources, and/or other resources that would otherwise have beenconsumed by generating a shortened URL that is not memorable and isconfusing for users, causing a device to access an incorrect networkresource associated with a shortened URL (when the characters of the URLare erroneously input into the device), causing a device to access amalicious network resource associated with a phishing attack, takingremedial actions against the phishing attacks, among other examples.

As indicated above, FIGS. 1A-1F are provided as an example. Otherexamples may differ from what is described with regard to FIGS. 1A-1F.The number and arrangement of devices shown in FIGS. 1A-1F are providedas an example. In practice, there may be additional devices, fewerdevices, different devices, or differently arranged devices than thoseshown in FIGS. 1A-1F. Furthermore, two or more devices shown in FIGS.1A-1F may be implemented within a single device, or a single deviceshown in FIGS. 1A-1F may be implemented as multiple, distributeddevices. Additionally, or alternatively, a set of devices (e.g., one ormore devices) shown in FIGS. 1A-1F may perform one or more functionsdescribed as being performed by another set of devices shown in FIGS.1A-1F.

FIG. 2 is a diagram illustrating an example 200 of training and using amachine learning model (e.g., the neural network models) in connectionwith generating a reference image based on a combination of images. Themachine learning model training and usage described herein may beperformed using a machine learning system. The machine learning systemmay include or may be included in a computing device, a server, a cloudcomputing environment, and/or the like, such as the reference systemdescribed in more detail elsewhere herein.

As shown by reference number 205, a machine learning model may betrained using a set of observations. The set of observations may beobtained from historical data, such as data gathered during one or moreprocesses described herein. In some implementations, the machinelearning system may receive the set of observations (e.g., as input)from the reference system, as described elsewhere herein.

As shown by reference number 210, the set of observations includes afeature set. The feature set may include a set of variables, and avariable may be referred to as a feature. A specific observation mayinclude a set of variable values (or feature values) corresponding tothe set of variables. In some implementations, the machine learningsystem may determine variables for a set of observations and/or variablevalues for a specific observation based on input received from thereference system. For example, the machine learning system may identifya feature set (e.g., one or more features and/or feature values) byextracting the feature set from structured data, by performing naturallanguage processing to extract the feature set from unstructured data,by receiving input from an operator, and/or the like.

As an example, a feature set for a set of observations may include afirst feature of a first image data, a second feature of second imagedata, a third feature of a memorability score, and so on. As shown, fora first observation, the first feature may have a value of first image1, the second feature may have a value of second image 1, the thirdfeature may have a value of memorability score 1, and so on. Thesefeatures and feature values are provided as examples and may differ inother examples.

As shown by reference number 215, the set of observations may beassociated with a target variable. The target variable may represent avariable having a numeric value, may represent a variable having anumeric value that falls within a range of values or has some discretepossible values, may represent a variable that is selectable from one ofmultiple options (e.g., one of multiple classes, classifications,labels, and/or the like), may represent a variable having a Booleanvalue, and/or the like. A target variable may be associated with atarget variable value, and a target variable value may be specific to anobservation. In example 200, the target variable is a reference image,which has a value of reference image 1 for the first observation.

The target variable may represent a value that a machine learning modelis being trained to predict, and the feature set may represent thevariables that are input to a trained machine learning model to predicta value for the target variable. The set of observations may includetarget variable values so that the machine learning model can be trainedto recognize patterns in the feature set that lead to a target variablevalue. A machine learning model that is trained to predict a targetvariable value may be referred to as a supervised learning model.

In some implementations, the machine learning model may be trained on aset of observations that do not include a target variable. This may bereferred to as an unsupervised learning model. In this case, the machinelearning model may learn patterns from the set of observations withoutlabeling or supervision, and may provide output that indicates suchpatterns, such as by using clustering and/or association to identifyrelated groups of items within the set of observations.

As shown by reference number 220, the machine learning system may traina machine learning model using the set of observations and using one ormore machine learning algorithms, such as a regression algorithm, adecision tree algorithm, a neural network algorithm, a k-nearestneighbor algorithm, a support vector machine algorithm, and/or the like.After training, the machine learning system may store the machinelearning model as a trained machine learning model 225 to be used toanalyze new observations.

As shown by reference number 230, the machine learning system may applythe trained machine learning model 225 to a new observation, such as byreceiving a new observation and inputting the new observation to thetrained machine learning model 225. As shown, the new observation mayinclude a first feature of first image X, a second feature of secondimage X, a third feature of memorability score X, and so on, as anexample. The machine learning system may apply the trained machinelearning model 225 to the new observation to generate an output (e.g., aresult). The type of output may depend on the type of machine learningmodel and/or the type of machine learning task being performed. Forexample, the output may include a predicted value of a target variable,such as when supervised learning is employed. Additionally, oralternatively, the output may include information that identifies acluster to which the new observation belongs, information that indicatesa degree of similarity between the new observation and one or more otherobservations, and/or the like, such as when unsupervised learning isemployed.

As an example, the trained machine learning model 225 may predict avalue of reference image X for the target variable of the memorabilityscore for the new observation, as shown by reference number 235. Basedon this prediction, the machine learning system may provide a firstrecommendation, may provide output for determination of a firstrecommendation, may perform a first automated action, may cause a firstautomated action to be performed (e.g., by instructing another device toperform the automated action), and/or the like.

In some implementations, the trained machine learning model 225 mayclassify (e.g., cluster) the new observation in a cluster, as shown byreference number 240. The observations within a cluster may have athreshold degree of similarity. As an example, if the machine learningsystem classifies the new observation in a first cluster (e.g., a firstimage data cluster), then the machine learning system may provide afirst recommendation. Additionally, or alternatively, the machinelearning system may perform a first automated action and/or may cause afirst automated action to be performed (e.g., by instructing anotherdevice to perform the automated action) based on classifying the newobservation in the first cluster.

As another example, if the machine learning system were to classify thenew observation in a second cluster (e.g., a second image data cluster),then the machine learning system may provide a second (e.g., different)recommendation and/or may perform or cause performance of a second(e.g., different) automated action.

In some implementations, the recommendation and/or the automated actionassociated with the new observation may be based on a target variablevalue having a particular label (e.g., classification, categorization,and/or the like), may be based on whether a target variable valuesatisfies one or more thresholds (e.g., whether the target variablevalue is greater than a threshold, is less than a threshold, is equal toa threshold, falls within a range of threshold values, and/or the like),may be based on a cluster in which the new observation is classified,and/or the like.

In this way, the machine learning system may apply a rigorous andautomated process to generate a reference image based on a combinationof images. The machine learning system enables recognition and/oridentification of tens, hundreds, thousands, or millions of featuresand/or feature values for tens, hundreds, thousands, or millions ofobservations, thereby increasing accuracy and consistency and reducingdelay associated with generating a reference image based on acombination of images relative to requiring computing resources to beallocated for tens, hundreds, or thousands of operators to manuallygenerate initiative plans.

As indicated above, FIG. 2 is provided as an example. Other examples maydiffer from what is described in connection with FIG. 2.

FIG. 3 is a diagram of an example environment 300 in which systemsand/or methods described herein may be implemented. As shown in FIG. 3,environment 300 may include a reference system 301, which may includeone or more elements of and/or may execute within a cloud computingsystem 302. The cloud computing system 302 may include one or moreelements 303-313, as described in more detail below. As further shown inFIG. 3, environment 300 may include a network 320 and/or a user device330. Devices and/or elements of environment 300 may interconnect viawired connections and/or wireless connections.

The cloud computing system 302 includes computing hardware 303, aresource management component 304, a host operating system (OS) 305,and/or one or more virtual computing systems 306. The resourcemanagement component 304 may perform virtualization (e.g., abstraction)of computing hardware 303 to create the one or more virtual computingsystems 306. Using virtualization, the resource management component 304enables a single computing device (e.g., a computer, a server, and/orthe like) to operate like multiple computing devices, such as bycreating multiple isolated virtual computing systems 306 from computinghardware 303 of the single computing device. In this way, computinghardware 303 can operate more efficiently, with lower power consumption,higher reliability, higher availability, higher utilization, greaterflexibility, and lower cost than using separate computing devices.

Computing hardware 303 includes hardware and corresponding resourcesfrom one or more computing devices. For example, computing hardware 303may include hardware from a single computing device (e.g., a singleserver) or from multiple computing devices (e.g., multiple servers),such as multiple computing devices in one or more data centers. Asshown, computing hardware 303 may include one or more processors 307,one or more memories 308, one or more storage components 309, and/or oneor more networking components 310. Examples of a processor, a memory, astorage component, and a networking component (e.g., a communicationcomponent) are described elsewhere herein.

The resource management component 304 includes a virtualizationapplication (e.g., executing on hardware, such as computing hardware303) capable of virtualizing computing hardware 303 to start, stop,and/or manage one or more virtual computing systems 306. For example,the resource management component 304 may include a hypervisor (e.g., abare-metal or Type 1 hypervisor, a hosted or Type 2 hypervisor, and/orthe like) or a virtual machine monitor, such as when the virtualcomputing systems 306 are virtual machines 311. Additionally, oralternatively, the resource management component 304 may include acontainer manager, such as when the virtual computing systems 306 arecontainers 312. In some implementations, the resource managementcomponent 304 executes within and/or in coordination with a hostoperating system 305.

A virtual computing system 306 includes a virtual environment thatenables cloud-based execution of operations and/or processes describedherein using computing hardware 303. As shown, a virtual computingsystem 306 may include a virtual machine 311, a container 312, a hybridenvironment 313 that includes a virtual machine and a container, and/orthe like. A virtual computing system 306 may execute one or moreapplications using a file system that includes binary files, softwarelibraries, and/or other resources required to execute applications on aguest operating system (e.g., within the virtual computing system 306)or the host operating system 305.

Although the reference system 301 may include one or more elements303-313 of the cloud computing system 302, may execute within the cloudcomputing system 302, and/or may be hosted within the cloud computingsystem 302, in some implementations, the reference system 301 may not becloud-based (e.g., may be implemented outside of a cloud computingsystem) or may be partially cloud-based. For example, the referencesystem 301 may include one or more devices that are not part of thecloud computing system 302, such as device 400 of FIG. 4, which mayinclude a standalone server or another type of computing device. Thereference system 301 may perform one or more operations and/or processesdescribed in more detail elsewhere herein.

Network 320 includes one or more wired and/or wireless networks. Forexample, network 320 may include a cellular network, a public landmobile network (PLMN), a local area network (LAN), a wide area network(WAN), a private network, the Internet, and/or the like, and/or acombination of these or other types of networks. The network 320 enablescommunication among the devices of environment 300.

User device 330 includes one or more devices capable of receiving,generating, storing, processing, and/or providing information, asdescribed elsewhere herein. User device 330 may include a communicationdevice. For example, user device 330 may include a wirelesscommunication device, a user equipment (UE), a mobile phone (e.g., asmart phone or a cell phone, among other examples), a laptop computer, atablet computer, a handheld computer, a desktop computer, a gamingdevice, a wearable communication device (e.g., a smart wristwatch or apair of smart eyeglasses, among other examples), an Internet of Things(IoT) device, or a similar type of device. User device 330 maycommunicate with one or more other devices of environment 300, asdescribed elsewhere herein.

The number and arrangement of devices and networks shown in FIG. 3 areprovided as an example. In practice, there may be additional devicesand/or networks, fewer devices and/or networks, different devices and/ornetworks, or differently arranged devices and/or networks than thoseshown in FIG. 3. Furthermore, two or more devices shown in FIG. 3 may beimplemented within a single device, or a single device shown in FIG. 3may be implemented as multiple, distributed devices. Additionally, oralternatively, a set of devices (e.g., one or more devices) ofenvironment 300 may perform one or more functions described as beingperformed by another set of devices of environment 300.

FIG. 4 is a diagram of example components of a device 400, which maycorrespond to reference system 301 and/or user device 330. In someimplementations, reference system 301 and/or user device 330 may includeone or more devices 400 and/or one or more components of device 400. Asshown in FIG. 4, device 400 may include a bus 410, a processor 420, amemory 430, a storage component 440, an input component 450, an outputcomponent 460, and a communication component 470.

Bus 410 includes a component that enables wired and/or wirelesscommunication among the components of device 400. Processor 420 includesa central processing unit, a graphics processing unit, a microprocessor,a controller, a microcontroller, a digital signal processor, afield-programmable gate array, an application-specific integratedcircuit, and/or another type of processing component. Processor 420 isimplemented in hardware, firmware, or a combination of hardware andsoftware. In some implementations, processor 420 includes one or moreprocessors capable of being programmed to perform a function. Memory 430includes a random-access memory, a read only memory, and/or another typeof memory (e.g., a flash memory, a magnetic memory, and/or an opticalmemory).

Storage component 440 stores information and/or software related to theoperation of device 400. For example, storage component 440 may includea hard disk drive, a magnetic disk drive, an optical disk drive, asolid-state disk drive, a compact disc, a digital versatile disc, and/oranother type of non-transitory computer-readable medium. Input component450 enables device 400 to receive input, such as user input and/orsensed inputs. For example, input component 450 may include a touchscreen, a keyboard, a keypad, a mouse, a button, a microphone, a switch,a sensor, a global positioning system component, an accelerometer, agyroscope, an actuator, and/or the like. Output component 460 enablesdevice 400 to provide output, such as via a display, a speaker, and/orone or more light-emitting diodes. Communication component 470 enablesdevice 400 to communicate with other devices, such as via a wiredconnection and/or a wireless connection. For example, communicationcomponent 470 may include a receiver, a transmitter, a transceiver, amodem, a network interface card, an antenna, and/or the like.

Device 400 may perform one or more processes described herein. Forexample, a non-transitory computer-readable medium (e.g., memory 430and/or storage component 440) may store a set of instructions (e.g., oneor more instructions, code, software code, program code, and/or thelike) for execution by processor 420. Processor 420 may execute the setof instructions to perform one or more processes described herein. Insome implementations, execution of the set of instructions, by one ormore processors 420, causes the one or more processors 420 and/or thedevice 400 to perform one or more processes described herein. In someimplementations, hardwired circuitry may be used instead of or incombination with the instructions to perform one or more processesdescribed herein. Thus, implementations described herein are not limitedto any specific combination of hardware circuitry and software.

The number and arrangement of components shown in FIG. 4 are provided asan example. Device 400 may include additional components, fewercomponents, different components, or differently arranged componentsthan those shown in FIG. 4. Additionally, or alternatively, a set ofcomponents (e.g., one or more components) of device 400 may perform oneor more functions described as being performed by another set ofcomponents of device 400.

FIG. 5 is a flowchart of an example process 500 for utilizing a neuralnetwork model to generate a reference image based on a combination ofimages. In some implementations, one or more process blocks of FIG. 5may be performed by a device (e.g., reference system 301). In someimplementations, one or more process blocks of FIG. 5 may be performedby another device or a group of devices separate from or including thedevice, such as a user device (e.g., user device 330). Additionally, oralternatively, one or more process blocks of FIG. 5 may be performed byone or more components of device 400, such as processor 420, memory 430,storage component 440, input component 450, output component 460, and/orcommunication component 470.

As shown in FIG. 5, process 500 may include receiving complex data froma user device (block 505). For example, the device may receive complexdata from a user device, as described above.

As further shown in FIG. 5, process 500 may include providing aplurality of images to the user device based on receiving the complexdata (block 510). For example, the device may provide a plurality ofimages to the user device based on receiving the complex data, asdescribed above. The complex data includes one or more of a uniformresource locator, a telephone number, or textual information. Theplurality of images may include images associated with one or more ofcolors, fruits, animals, or locations.

As further shown in FIG. 5, process 500 may include receiving, from theuser device, a selection of two or more images from the plurality ofimages (block 515). For example, the device may receive, from the userdevice, a selection of two or more images from the plurality of images,as described above.

As further shown in FIG. 5, process 500 may include determining whethera combination of the two or more images are stored in a data structure(block 520). For example, the device may determine whether thecombination of the two or more images are stored in a data structure, asdescribed above.

As further shown in FIG. 5, process 500 may include determining amapping of the two or more images with the complex data, based on thecombination of the two or more images not being stored in the datastructure (block 525). For example, the device may determine a mappingof information identifying the two or more images with the complex data,based on the combination of the two or more images not being stored inthe data structure, as described above.

As further shown in FIG. 5, process 500 may include storing informationidentifying the two or more images, the complex data, and the mapping inthe data structure (block 530). For example, the device may storeinformation identifying the two or more images, the complex data, andthe mapping in the data structure, as described above. The mappingcorrelates the one or more images with the complex data.

As further shown in FIG. 5, process 500 may include processing the twoor more images to generate a reference image that satisfies amemorability score threshold (block 535). For example, the device mayprocess the two or more images, with a neural network model, to generatea reference image that satisfies a memorability score threshold, asdescribed above. Processing the two or more images, with the neuralnetwork model, to generate the reference image includes combining theone or more images in different manners to generate a plurality ofcombined images, calculating a plurality of memorability scores for theplurality of combined images, and selecting one of the plurality ofcombined images as the reference image based on the plurality ofmemorability scores and the memorability score threshold.

As further shown in FIG. 5, process 500 may include providing thereference image to another user device (block 540). For example, thedevice may provide the reference image to another user device, asdescribed above. Providing the final image to the other user device maycomprise providing the final image to the other user device via awebsite.

As further shown in FIG. 5, process 500 may include receiving, from theother user device, a selection of the two or more images (block 545).For example, the device may receive, from the other user device, aselection of the two or more images, as described above.

As further shown in FIG. 5, process 500 may include retrieving thecomplex data from the data structure based on the mapping and based onthe selection of the two or more images (block 550). For example, thedevice may retrieve the complex data from the data structure based onthe mapping and based on the selection of the reference image, asdescribed above.

As further shown in FIG. 5, process 500 may include performing one ormore actions based on the complex data (block 555). For example, thedevice may perform one or more actions based on the complex data, asdescribed above. Performing the one or more actions may includeaccessing a website via a uniform resource locator associated with thecomplex data, and providing the website to the other user device.Performing the one or more actions may include receiving, from the otheruser device, feedback associated with the final image, based on thecomplex data, and modifying the final image based on the feedback.

Performing the one or more actions includes causing a call to atelephone number associated with the complex data to be placed by theother user device. Performing the one or more actions may include one ormore of providing text associated with the complex data to the otheruser device, or retraining the neural network model based on the complexdata. In some implementations, process 500 may include requesting, fromthe user device, a new selection of one or more new images, from theplurality of images, based on the one or more images conflicting withthe images stored in the data structure.

In some implementations, process 500 may include processing the one ormore images, with the neural network model, to generate the final imageincludes combining the one or more images in different manners togenerate a plurality of combined images, calculating a plurality ofmemorability scores for the plurality of combined images, and selectingone of the plurality of combined images as the final image based on theplurality of memorability scores and the memorability score threshold.

In some implementations, process 500 may include providing the finalimage to the user device prior to providing the final image to the otheruser device, receiving, from the user device, a modification to thefinal image, and modifying the final image based on the modificationprior to providing the final image to the other user device.

Although FIG. 5 shows example blocks of process 500, in someimplementations, process 500 may include additional blocks, fewerblocks, different blocks, or differently arranged blocks than thosedepicted in FIG. 5. Additionally, or alternatively, two or more of theblocks of process 500 may be performed in parallel.

The foregoing disclosure provides illustration and description, but isnot intended to be exhaustive or to limit the implementations to theprecise form disclosed. Modifications may be made in light of the abovedisclosure or may be acquired from practice of the implementations.

As used herein, the term “component” is intended to be broadly construedas hardware, firmware, or a combination of hardware and software. Itwill be apparent that systems and/or methods described herein may beimplemented in different forms of hardware, firmware, and/or acombination of hardware and software. The actual specialized controlhardware or software code used to implement these systems and/or methodsis not limiting of the implementations. Thus, the operation and behaviorof the systems and/or methods are described herein without reference tospecific software code—it being understood that software and hardwarecan be used to implement the systems and/or methods based on thedescription herein.

As used herein, satisfying a threshold may, depending on the context,refer to a value being greater than the threshold, greater than or equalto the threshold, less than the threshold, less than or equal to thethreshold, equal to the threshold, and/or the like, depending on thecontext.

Although particular combinations of features are recited in the claimsand/or disclosed in the specification, these combinations are notintended to limit the disclosure of various implementations. In fact,many of these features may be combined in ways not specifically recitedin the claims and/or disclosed in the specification. Although eachdependent claim listed below may directly depend on only one claim, thedisclosure of various implementations includes each dependent claim incombination with every other claim in the claim set.

No element, act, or instruction used herein should be construed ascritical or essential unless explicitly described as such. Also, as usedherein, the articles “a” and “an” are intended to include one or moreitems and may be used interchangeably with “one or more.” Further, asused herein, the article “the” is intended to include one or more itemsreferenced in connection with the article “the” and may be usedinterchangeably with “the one or more.” Furthermore, as used herein, theterm “set” is intended to include one or more items (e.g., relateditems, unrelated items, a combination of related and unrelated items,and/or the like), and may be used interchangeably with “one or more.”Where only one item is intended, the phrase “only one” or similarlanguage is used. Also, as used herein, the terms “has,” “have,”“having,” or the like are intended to be open-ended terms. Further, thephrase “based on” is intended to mean “based, at least in part, on”unless explicitly stated otherwise. Also, as used herein, the term “or”is intended to be inclusive when used in a series and may be usedinterchangeably with “and/or,” unless explicitly stated otherwise (e.g.,if used in combination with “either” or “only one of”).

What is claimed is:
 1. A method, comprising: receiving, by a device,complex data from a user device; providing, by the device, a pluralityof images to the user device based on receiving the complex data;receiving, by the device and from the user device, a selection of two ormore images from the plurality of images; determining, by the device,whether a combination of the two or more images is stored in a datastructure; determining, by the device, a mapping of the two or moreimages with the complex data, based on the combination of the two ormore images not being stored in the data structure; storing, by thedevice, information identifying the two or more images, the complexdata, and the mapping in the data structure; processing, by the device,the two or more images to generate a reference image that satisfies amemorability score threshold; providing, by the device, the referenceimage to another user device; receiving, by the device and from theother user device, a selection of the two or more images; retrieving, bythe device, the complex data from the data structure based on themapping and based on the selection of the two or more images; andperforming, by the device, one or more actions based on the complexdata.
 2. The method of claim 1, wherein the complex data includes one ormore of: a uniform resource locator, a telephone number, or textualinformation.
 3. The method of claim 1, wherein the two or more imagesare processed with a neural network model to generate the referenceimage.
 4. The method of claim 1, further comprising: requesting, fromthe user device, a new selection of two or more new images, from theplurality of images, based on the combination of the two or more imagesbeing stored in the data structure.
 5. The method of claim 1, whereinprocessing the two or more images to generate the reference imagecomprises: combining the two or more images in different manners togenerate a plurality of combined images; calculating, using a neuralnetwork model, a plurality of memorability scores for the plurality ofcombined images; and selecting one of the plurality of combined imagesas the reference image based on the plurality of memorability scores andthe memorability score threshold.
 6. The method of claim 1, furthercomprising: providing the reference image to the user device prior toproviding the reference image to the other user device; receiving, fromthe user device, a modification to the reference image; and modifyingthe reference image based on the modification prior to providing thereference image to the other user device.
 7. The method of claim 1,wherein providing the reference image to the other user devicecomprises: providing the reference image to the other user device via awebsite.
 8. A device, comprising: one or more memories; and one or moreprocessors, communicatively coupled to the one or more memories,configured to: receive complex data from a user device, wherein thecomplex data includes one or more of: a uniform resource locator, atelephone number, or textual information; provide a plurality of imagesto the user device based on receiving the complex data; receive, fromthe user device, a selection of two or more images from the plurality ofimages; determine whether a combination of the two or more images isstored in a data structure; determine a mapping of the two or moreimages with the complex data, based on the combination of the two ormore images not being stored in the data structure; store informationidentifying the two or more images, the complex data, and the mapping inthe data structure; process the two or more images to generate areference image that satisfies a memorability score threshold; providethe reference image to another user device; receive, from the other userdevice, a selection of the two or more images; retrieve the complex datafrom the data structure based on the mapping and based on the selectionof the two or more images; and perform one or more actions based on thecomplex data.
 9. The device of claim 8, wherein the one or moreprocessors, when performing the one or more actions, are configured to:access a website via a uniform resource locator associated with thecomplex data; and provide the website to the other user device.
 10. Thedevice of claim 8, wherein the one or more processors, when performingthe one or more actions, are configured to: receive, from the other userdevice, feedback associated with the reference image, based on thecomplex data; and modify the reference image based on the feedback. 11.The device of claim 8, wherein the one or more processors, whenperforming the one or more actions, are configured to: cause a call to atelephone number associated with the complex data to be established withthe other user device.
 12. The device of claim 8, wherein the two ormore images are processed with a neural network model to generate thereference image; wherein the one or more processors, when performing theone or more actions, are configured to one or more of: provide textassociated with the complex data to the other user device; or retrainthe neural network model based on the complex data.
 13. The device ofclaim 8, wherein the mapping correlates the two or more images with thecomplex data.
 14. The device of claim 8, wherein the one or moreprocessors are further configured to: request, from the user device, anew selection of two or more new images, from the plurality of images,based on the combination of the two or more images being stored in thedata structure.
 15. A non-transitory computer-readable medium storing aset of instructions, the set of instructions comprising: one or moreinstructions that, when executed by one or more processors of a device,cause the device to: receive complex data from a user device; provide aplurality of images to the user device based on receiving the complexdata; receive, from the user device, a selection of two or more imagesfrom the plurality of images; determine whether a combination of the twoor more images is stored in a data structure; determine a mapping of thetwo or more images with the complex data, based on the combination ofthe two or more images not being stored in the data structure; storeinformation identifying the two or more images, the complex data, andthe mapping in the data structure; process the two or more images togenerate a reference image that satisfies a memorability scorethreshold; and provide the reference image to another user device. 16.The non-transitory computer-readable medium of claim 15, wherein the oneor more instructions further cause the device to: receive, from theother user device, a selection of the two or more images; retrieve thecomplex data from the data structure based on the mapping and based onthe selection of the two or more images; and perform one or more actionsbased on the complex data.
 17. The non-transitory computer-readablemedium of claim 15, wherein the one or more instructions further causethe device to: request, from the user device, a new selection of two ormore new images, from the plurality of images, based on the combinationof the two or more images being stored in the data structure.
 18. Thenon-transitory computer-readable medium of claim 15, wherein the one ormore instructions, that cause the device to process the two or moreimages to generate the reference image, cause the device to: combine theone or more images in different manners to generate a plurality ofcombined images; calculate, using a neural network model, a plurality ofmemorability scores for the plurality of combined images; and select oneof the plurality of combined images as the reference image based on theplurality of memorability scores and the memorability score threshold.19. The non-transitory computer-readable medium of claim 15, wherein theone or more instructions further cause the device to: provide thereference image to the user device prior to providing the referenceimage to the other user device; receive, from the user device, amodification to the reference image; and modify the reference imagebased on the modification prior to providing the reference image to theother user device.
 20. The non-transitory computer-readable medium ofclaim 15, wherein the complex data includes one or more of: a uniformresource locator, a telephone number, or textual information.